1. Field of the Invention
The present invention relates to an image processing system, an image output device, and an image processing method.
2. Description of the Related Art
As image data is usually two-dimensional multi-valued data, the data volume of image data is enormously large compared to text data. In a case where image data is saved in a database and transmitted by a network, from the viewpoint of efficiently using limited resources (storage capacity and network band), the data volume is preferably reduced. For this reason, in general, the quality of the image data is reduced, by resolution conversion (decreasing the resolution) such as pixel thinning, for example, down sampling, etc., and by lossy compression such as JPEG (Joint Photographic Experts Group) compression, etc.
Meanwhile, there are problems accompanying the reduction of quality. By resolution conversion, the sense of resolution decreases as the number of pixels decreases due to pixel thinning, for example, down sampling, and the image gives an overall blurry impression. Lossy compression is widely used as the data volume can be effectively reduced without changing the image size; however, the compression efficiency and the image quality are in a tradeoff relationship. Particularly, by JPEG compression, unintended patterns are generated, which are referred to as mosquito noise and block noise, and therefore the appearance of the image is deteriorated. As a method of mitigating noise generated in the image, for example, there is a smoothing process of suppressing high-frequency components in which noise is apt to be generated. However, the high-frequency components included in the original image are also suppressed, which often leads to a decrease in the sense of resolution.
As methods of improving the decrease in the sense of resolution caused by resolution conversion and a smoothing process, there are a sharpening process of emphasizing the edges, and a super-resolution process of supplementing the outline part and the texture part with information. Generally, the latter is known to have higher effects in increasing the sense of resolution, than the former.
As a method of the super-resolution process, there is a method referred to as learning-type super-resolution. The learning-type super-resolution can be roughly divided into a learning process and a super-resolution process. In the learning process, the process of deteriorating the image is learned by using multiple training images that are prepared in advance, and a dictionary is constructed, which stores patterns before deterioration and patterns after deterioration. In the super-resolution process, the resolution is improved by supplementing the image whose resolution has decreased, with high frequency components, by referring to the dictionary constructed in the learning process.
Furthermore, in a conventionally known learning-type super-resolution technology, in the learning process, a plurality of pairs of image patches are registered in the dictionary. A pair of image patches are images of small areas (patches) corresponding to each other, which are respectively cut out from the training image and from a low-resolution image obtained by decreasing the resolution of the training image. In the super-resolution process, an input image is supplemented with high-frequency components by using the dictionary (see Patent Document 1, Non-patent Document 1).
Furthermore, in another conventionally known technology, in the learning process, multiple pairs of image patches are used. The image patches are resolved into a basic structural element referred to as a base, to construct a dictionary (see Non-patent Document 2).
The conventional technology is proposed from the viewpoint of how to restore an original image to have high-quality, under conditions where only a low-quality input image can be obtained (if a high-quality input image is obtained in the first place, there is no need to restore the input image), and multiple high-quality training images, which have been prepared separately, are used to construct the dictionary. Therefore, in order to construct a dictionary that can handle any low-quality image, the size of the dictionary becomes enormous, which leads to an increase in the calculation time in the learning process and the super-resolution process.
Furthermore, in the conventional technology, consideration is not made with respect to problems that arise when transmitting the input low-quality image by a network, etc. When low-quality images are sent for the purpose of network transmission or database storage, and the low-quality images are restored by learning-type super-resolution at the reception destination where the image received, a dictionary needs to be prepared in advance at the reception destination. However, it is difficult to prepare training images for handling any image.
Patent Document 1: Japanese Laid-Open Patent Publication No. 2003-018398
Non-patent Document 1: Senda, et. al, “Example-based Super Resolution to Achieve Fine Magnification of Low-Resolution Images”, NEC Technical Journal, Vol. 65, No. 2 (2012)
Non-patent Document 2: J. Yang, et. al, “Image Super-Resolution via Sparse Representation,” IEEE Transactions on Image Processing, Vol. 19, Issue 11, pp. 1-13 (2010)